




'''simple docstring'''



import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil





class      lowerCAmelCase :


       def __init__( self     ,	__SCREAMING_SNAKE_CASE = "cpu"     ,	__SCREAMING_SNAKE_CASE = "openai/clip-vit-large-patch14"	)   ->	None:



            '''simple docstring'''




            __snake_case            =	device
            __snake_case            =	CLIPTokenizerFast.from_pretrained(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	[0.48_145_466, 0.4_578_275, 0.40_821_073]
            __snake_case            =	[0.26_862_954, 0.26_130_258, 0.27_577_711]
            __snake_case            =	torchvision.transforms.Normalize(self.image_mean     ,	self.image_std	)
            __snake_case            =	torchvision.transforms.Resize(224	)
            __snake_case            =	torchvision.transforms.CenterCrop(224	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.resize(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.center_crop(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.normalize(__SCREAMING_SNAKE_CASE	)
            return images




       def __call__( self     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	**__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.tokenizer(text=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.preprocess_img(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	{key: value.to(self.device	) for (key, value) in encoding.items()}
            return encoding





class      lowerCAmelCase (			nn.Module):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE=0.01     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE="image"     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=False     ,	)   ->	None:



            '''simple docstring'''




            super().__init__()
            __snake_case            =	None
            __snake_case            =	device if device else get_device()
            if vqgan:
                 __snake_case            =	vqgan
            else:
                 __snake_case            =	load_vqgan(self.device     ,	conf_path=__SCREAMING_SNAKE_CASE     ,	ckpt_path=__SCREAMING_SNAKE_CASE	)
            self.vqgan.eval()
            if clip:
                 __snake_case            =	clip
            else:
                 __snake_case            =	CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''	)
            self.clip.to(self.device	)
            __snake_case            =	ProcessorGradientFlow(device=self.device	)

            __snake_case            =	iterations
            __snake_case            =	lr
            __snake_case            =	log
            __snake_case            =	make_grid
            __snake_case            =	return_val
            __snake_case            =	quantize
            __snake_case            =	self.vqgan.decoder.z_shape


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=5     ,	__SCREAMING_SNAKE_CASE=True	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	[]
            if output_path is None:
                 __snake_case            =	'''./animation.gif'''
            if input_path is None:
                 __snake_case            =	self.save_path
            __snake_case            =	sorted(glob(input_path + '''/*'''	)	)
            if not len(__SCREAMING_SNAKE_CASE	):
                 raise ValueError(
                     '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
                     ''' function?)'''	)
            if len(__SCREAMING_SNAKE_CASE	) == 1:
                 print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''	)
            __snake_case            =	total_duration / len(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	[frame_duration] * len(__SCREAMING_SNAKE_CASE	)
            if extend_frames:
                 __snake_case            =	1.5
                 __snake_case            =	3
            for file_name in paths:
                 if file_name.endswith('''.png'''	):
                      images.append(imageio.imread(__SCREAMING_SNAKE_CASE	)	)
            imageio.mimsave(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	duration=__SCREAMING_SNAKE_CASE	)
            print(F'''gif saved to {output_path}'''	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None	)   ->	List[Any]:



            '''simple docstring'''




            if not (path or img):
                 raise ValueError('''Input either path or tensor'''	)
            if img is not None:
                 raise NotImplementedError
            __snake_case            =	preprocess(Image.open(__SCREAMING_SNAKE_CASE	)     ,	target_image_size=256	).to(self.device	)
            __snake_case            =	preprocess_vqgan(__SCREAMING_SNAKE_CASE	)
            __snake_case       ,     *__snake_case            =	self.vqgan.encode(__SCREAMING_SNAKE_CASE	)
            return z


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	self.latent.detach().requires_grad_()
            __snake_case            =	base_latent + transform_vector
            if self.quantize:
                 __snake_case       ,     *__snake_case            =	self.vqgan.quantize(__SCREAMING_SNAKE_CASE	)
            else:
                 __snake_case            =	trans_latent
            return self.vqgan.decode(__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	self.clip_preprocessor(text=__SCREAMING_SNAKE_CASE     ,	images=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''     ,	padding=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.clip(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	clip_outputs.logits_per_image
            if weights is not None:
                 __snake_case            =	similarity_logits * weights
            return similarity_logits.sum()


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self._get_clip_similarity(pos_prompts['''prompts''']     ,	__SCREAMING_SNAKE_CASE     ,	weights=(1 / pos_prompts['''weights'''])	)
            if neg_prompts:
                 __snake_case            =	self._get_clip_similarity(neg_prompts['''prompts''']     ,	__SCREAMING_SNAKE_CASE     ,	weights=neg_prompts['''weights''']	)
            else:
                 __snake_case            =	torch.tensor([1]     ,	device=self.device	)
            __snake_case            =	-torch.log(__SCREAMING_SNAKE_CASE	) + torch.log(__SCREAMING_SNAKE_CASE	)
            return loss


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	torch.randn_like(self.latent     ,	requires_grad=__SCREAMING_SNAKE_CASE     ,	device=self.device	)
            __snake_case            =	torch.optim.Adam([vector]     ,	lr=self.lr	)

            for i in range(self.iterations	):
                 optim.zero_grad()
                 __snake_case            =	self._add_vector(__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	loop_post_process(__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	self._get_CLIP_loss(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)
                 print('''CLIP loss'''     ,	__SCREAMING_SNAKE_CASE	)
                 if self.log:
                      wandb.log({'''CLIP Loss''': clip_loss}	)
                 clip_loss.backward(retain_graph=__SCREAMING_SNAKE_CASE	)
                 optim.step()
                 if self.return_val == "image":
                      yield custom_to_pil(transformed_img[0]	)
                 else:
                      yield vector


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Optional[Any]:



            '''simple docstring'''




            wandb.init(reinit=__SCREAMING_SNAKE_CASE     ,	project='''face-editor'''	)
            wandb.config.update({'''Positive Prompts''': positive_prompts}	)
            wandb.config.update({'''Negative Prompts''': negative_prompts}	)
            wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations}	)
            if image_path:
                 __snake_case            =	Image.open(__SCREAMING_SNAKE_CASE	)
                 __snake_case            =	image.resize((256, 256)	)
                 wandb.log('''Original Image'''     ,	wandb.Image(__SCREAMING_SNAKE_CASE	)	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	List[str]:



            '''simple docstring'''




            if not prompts:
                 return []
            __snake_case            =	[]
            __snake_case            =	[]
            if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	[prompt.strip() for prompt in prompts.split('''|'''	)]
            for prompt in prompts:
                 if isinstance(__SCREAMING_SNAKE_CASE     ,	(tuple, list)	):
                      __snake_case            =	prompt[0]
                      __snake_case            =	float(prompt[1]	)
                 elif ":" in prompt:
                      __snake_case       ,     __snake_case            =	prompt.split(''':'''	)
                      __snake_case            =	float(__SCREAMING_SNAKE_CASE	)
                 else:
                      __snake_case            =	prompt
                      __snake_case            =	1.0
                 processed_prompts.append(__SCREAMING_SNAKE_CASE	)
                 weights.append(__SCREAMING_SNAKE_CASE	)
            return {
                "prompts": processed_prompts,
                "weights": torch.tensor(__SCREAMING_SNAKE_CASE     ,	device=self.device	),
            }




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=None     ,	)   ->	int:



            '''simple docstring'''




            if image_path:
                 __snake_case            =	self._get_latent(__SCREAMING_SNAKE_CASE	)
            else:
                 __snake_case            =	torch.randn(self.latent_dim     ,	device=self.device	)
            if self.log:
                 self._init_logging(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            assert pos_prompts, "You must provide at least one positive prompt."
            __snake_case            =	self.process_prompts(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.process_prompts(__SCREAMING_SNAKE_CASE	)
            if save_final and save_path is None:
                 __snake_case            =	os.path.join('''./outputs/'''     ,	'''_'''.join(pos_prompts['''prompts''']	)	)
            if not os.path.exists(__SCREAMING_SNAKE_CASE	):
                 os.makedirs(__SCREAMING_SNAKE_CASE	)
            else:
                 __snake_case            =	save_path + '''_''' + get_timestamp()
                 os.makedirs(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	save_path

            __snake_case            =	self.vqgan.decode(self.latent	)[0]
            if show_intermediate:
                 print('''Original Image'''	)
                 show_pil(custom_to_pil(__SCREAMING_SNAKE_CASE	)	)

            __snake_case            =	loop_post_process(__SCREAMING_SNAKE_CASE	)
            for iter, transformed_img in enumerate(self._optimize_CLIP(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)	):
                 if show_intermediate:
                      show_pil(__SCREAMING_SNAKE_CASE	)
                 if save_intermediate:
                      transformed_img.save(os.path.join(self.save_path     ,	F'''iter_{iter:03d}.png'''	)	)
                 if self.log:
                      wandb.log({'''Image''': wandb.Image(__SCREAMING_SNAKE_CASE	)}	)
            if show_final:
                 show_pil(__SCREAMING_SNAKE_CASE	)
            if save_final:
                 transformed_img.save(os.path.join(self.save_path     ,	F'''iter_{iter:03d}_final.png'''	)	)










"""simple docstring"""



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]	)						-> Union[str, Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			os.path.join(args.tf_model_dir			,   '''parameters.json'''	)
	lowerCAmelCase_      							=			json.loads(open(_lowercase	).read()	)
	if not params:
		raise ValueError(
		    F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""	)
	if not args.output.endswith('''.pt'''	):
		lowerCAmelCase_      							=			args.output + '''.pt'''
	lowerCAmelCase_      							=			OrderedDict()
	with tf.device('''/CPU:0'''	):
		lowerCAmelCase_      							=			tf.train.load_checkpoint(args.tf_model_dir	)
		lowerCAmelCase_      							=			reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			lowerCAmelCase_      							=			reader.get_tensor(_lowercase	).astype(np.floataa	)
			if key_name.endswith('''/adam_m'''	) or key_name.endswith('''/adam_v'''	):
				continue
			if key_name.startswith('''pasts/'''	):
				if key_name.startswith('''pasts/mlp'''	):
					lowerCAmelCase_      							=			int(key_name[9]	)
				elif key_name.startswith('''pasts/out'''	):
					lowerCAmelCase_      							=			8
				lowerCAmelCase_      							=			'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/moe'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/switch_gating/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/softmlp/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/wo/kernel'''	) or key_name.endswith('''/wi/kernel'''	):
					lowerCAmelCase_      							=			key_name[-9:-7]
					for i in range(1_6	):
						lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
						lowerCAmelCase_      							=			(
						    vnp[i].transpose([1, 0]	).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/mlp'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/p1/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p1/bias'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p2/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p2/bias'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/ln'''	):
				lowerCAmelCase_      							=			int(key_name[8:].split('''/'''	)[0]	)
				if key_name.endswith('''/b'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.norm.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/g'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.norm.weight''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/att'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/qkv/kernel'''	):
					lowerCAmelCase_      							=			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					lowerCAmelCase_      							=			state[:, 0, :, :]
					lowerCAmelCase_      							=			state[:, 1, :, :]
					lowerCAmelCase_      							=			state[:, 2, :, :]
					lowerCAmelCase_      							=			(
					    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			(
					    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			(
					    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/o/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
					lowerCAmelCase_      							=			(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/an'''	):
				lowerCAmelCase_      							=			int(key_name[8:].split('''/'''	)[0]	)
				if key_name.endswith('''/b'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.norm.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/g'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.norm.weight''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif (
			    key_name.startswith('''model/wte'''	)
			    or key_name.startswith('''model/wpe'''	)
			    or key_name.startswith('''model/ete'''	)
			):
				lowerCAmelCase_      							=			{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
				    key_name[-3:]
				]
				lowerCAmelCase_      							=			'''model.%s.weight''' % nlayer
				lowerCAmelCase_      							=			vnp.copy()  # same in embedded
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				if key_name.startswith('''model/wte'''	):
					lowerCAmelCase_      							=			'''lm_head.weight'''
					lowerCAmelCase_      							=			vnp.copy()  # same in embedded
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/wob'''	):
				lowerCAmelCase_      							=			'''final_logits_bias'''
				lowerCAmelCase_      							=			vnp.copy()  # same in embedded
				lowerCAmelCase_      							=			state.reshape((1, -1)	)
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name == "model/dense/kernel":
				lowerCAmelCase_      							=			'''model.last_project.weight'''
				lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name == "model/dense_1/bias":
				lowerCAmelCase_      							=			'''model.last_project.bias'''
				lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
	torch.save(_lowercase			,   args.output	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser(
		    description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
		parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
		lowercase_							=							parser.parse_args()
		convert_tf_gptsan_to_pt(args)